Agile Work Environments

90% of startups don’t fail because of: Bad marketing, a weak team, or even a poor product. They fail because they lack a repeatable decision-making process. Here’s the framework I use to make better, faster decisions in business. I call it “The Iteration Loop.” It’s a structured way to identify what’s working, what’s broken, and what to do next, without getting stuck in endless guesswork. It gives you a systematic way to eliminate bottlenecks, optimize execution, and scale with clarity. Here are the 6 phases: 1. Bottleneck Identification 2. Clarifying the Goal 3. Solution Brainstorming 4. Focused Execution 5. Performance Review 6. Iterate & Improve 1️⃣ Bottleneck Identification Before you can fix anything, you need to identify the real problem. Most entrepreneurs spin their wheels solving the wrong issues because they never dig deep enough. To get clarity, ask: + What's the biggest constraint stopping growth right now? + What metric, if doubled, would create the biggest impact? + What’s preventing us from getting there? If you don’t identify the root problem, every solution you apply will be wasted effort. 2️⃣ Clarifying the Goal Once you know the problem, define the exact outcome you’re solving for. I use a simple Three-Part Goal Formula: 1. What are we trying to achieve? 2. By when? 3. What constraints do we have? Vague goals lead to vague actions. Precision forces progress. 3️⃣ Solution Brainstorming Now, generate every possible solution—without filtering. Most people limit themselves to their existing knowledge, which is why they get stuck. Instead, ask: “If there were no rules, what would I do?” This opens up better, faster, and often simpler solutions you wouldn’t have otherwise considered. 4️⃣ Focused Execution Don’t test everything at once—test one variable at a time. Most teams waste months by making too many changes at once, leading to messy, inconclusive results. Instead, break it down: 1. Test one key assumption. 2. Measure one KPI that proves or disproves it. 3. Execute for a set period, then review. 4. Speed matters. Complexity kills momentum. 5️⃣ Performance Review Your data isn’t just numbers—it’s feedback on your decision-making process. Your job is to analyze: + Did the solution work? + Why or why not? + What does this tell us about our business? Every test refines your ability to make better future decisions. 6️⃣ Iterate & Improve Most companies don’t fail from making the wrong move—they fail from making no moves at all. The only way to win long-term is to keep iterating. Instead of fearing failure, build a culture that rewards learning. Failure + Reflection = Progress. If you aren’t improving your decision-making process, your business will eventually hit a ceiling. That’s why I built The Iteration Loop—so every problem becomes an opportunity for better, faster execution. P.S. If you want the scaling roadmap I used to scale 3 businesses to $100M and beyond, you can get it for free from the link in my profile.

𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐭𝐢𝐧𝐠 𝐏𝐃𝐂𝐀 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐟𝐨𝐫 𝐂𝐨𝐧𝐭𝐢𝐧𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 🎯 Are your processes truly improving, or are you just firefighting? The PDCA Cycle (Plan-Do-Check-Act) is a simple yet powerful methodology for problem-solving and continuous improvement. It helps organizations move from reactive fixes to sustainable improvements. 🔄 What is PDCA? PDCA is a four-step, iterative cycle used for continuous improvement in processes, products, and systems. It ensures that changes are planned, tested, verified, and standardized before full-scale implementation. 📌 Also known as: Deming Cycle or Shewhart Cycle ❶PLAN - Build the Foundation Focus: Identify problems and develop an effective action plan. Plan Steps: ✅ Identify – Define the problem or opportunity for improvement. ✅ Observe – Gather data, facts, and insights. ✅ Analyze – Use tools like Fishbone Diagrams, 5 Why’s, and Pareto Analysis to find root causes. ✅ Action Plan – Develop solutions and define measurable goals, responsibilities, and timelines. 🔹 Example: A manufacturing company identifies high defect rates in its final product. After analysis, it finds that poor material handling is the root cause. ❷ DO - Implement the Solution Focus: Execute the plan on a small scale to test its effectiveness. ✅ Implement changes in a controlled environment. ✅ Train employees and document the process. ✅ Monitor real-time data to assess impact. 🔹 Example: The company introduces a new material handling procedure in one production line to test if defect rates decrease. ❸CHECK - Measure the Results Focus: Verify whether the changes lead to improvement. ✅ Compare results against planned objectives. ✅ Conduct inspections, audits, and feedback sessions. ✅ Identify any gaps or unintended issues. 🔹 Example: After one month, defect rates drop by 20%, confirming the effectiveness of the new process. ❹ ACT - Standardize & Scale Up Focus: Implement successful changes across the organization. ✅ Standardize the improved process. ✅ Create SOPs (Standard Operating Procedures) and training materials. ✅ Plan for continuous monitoring and future improvements. 🔹 Example: The new material handling procedure is rolled out across all production lines, and employees receive training to maintain consistency. 🔥 Hot Tips for PDCA Success: ✔️ Data First! Never assume—use facts and evidence. ✔️ Think Big, Start Small. Pilot solutions before full-scale implementation. ✔️ Involve Your Team. Collaboration leads to better problem-solving. ✔️ Measure Everything. If you can’t measure it, you can’t improve it. ✔️ Keep Iterating. PDCA is a cycle, not a one-time activity! 🔍 Are you using the PDCA cycle in your organization? Share your experiences in the comments! 👇 =============== 🔔 Consider following me at Govind Tiwari,PhD #Quality #PDCA #ContinuousImprovement #Lean #ProblemSolving #ProcessImprovement #qms #iso9001

One of the most fascinating projects I have worked on eventually became US Patent… a system for multi-modal journey optimization. At first glance, it sounds straightforward: get a traveler from point A to point B as quickly as possible. But in reality, this is not a “shortest path” problem. It is a problem of navigating combinatorial explosion under uncertainty while still producing results that humans will actually use. The lesson was simple, but profound: a single “optimal” route is often the wrong answer. In practice, commuters do not blindly follow whatever the algorithm declares “fastest.” They balance hidden costs (number of transfers, reliability, waiting time) against raw travel time. A route that is one minute slower but has one fewer transfer will often be preferred. We approached this by abandoning the idea of returning just one solution. Instead, we designed an iterative search that keeps a fixed-length priority queue of candidate paths, pruning aggressively to keep the search tractable, but always preserving multiple high-quality alternatives. The output is a set of Pareto-efficient options: fast, but also different enough that a user can choose the one that fits their risk tolerance, comfort level, or schedule flexibility. This project shifted how I think about optimization. The real challenge isn’t mathematical purity, it is making decisions robust to the messiness of the real world. If the solution space is reduced to a single “optimal” point, you risk oversimplifying reality and delivering something no one wants to use. When we expose the trade-offs explicitly, we help people make better decisions.

Clients want speed. Models demand accuracy. That tension shows up in every FEA project. I learned this the hard way on a job where the client wanted results in 2 hours even though the fine mesh needed 8 hours to solve. So I built an 80/20 meshing strategy that delivers most of the accuracy with a fraction of the cost. I broke the model into refinement zones that match the actual physics instead of spreading elements everywhere. → Ultra-fine mesh at 0.5–1 mm in the exact stress hot spots. → Medium mesh at 2–5 mm along the secondary load paths. → Coarse mesh at 10–20 mm in the bulk material that barely carries load. This keeps the solver focused where it matters. Then I use a short list of time savers that always pay off. → Symmetry to cut solve time by 2–4x. → Submodeling to refine only the areas that need detail. → Adaptive meshing to let the solver chase the critical regions for me. → Remote computing to spread the job across more cores. A recent project shows how much this changes the outcome. The initial fine mesh had 12 million elements and needed 18 hours to solve. The optimized mesh had 2 million elements and finished in 3 hours. The accuracy shift in the critical results stayed under 3 percent. The client signed off and the deadline was met without stress. Now I use a simple decision matrix to move faster. → Tight deadline means coarse mesh with targeted refinement. → Critical design means fine mesh with a convergence study. → Rapid iteration means adaptive and parametric meshing. Start coarse when exploring the design. Refine only when locking in the final validation. How do you balance mesh quality with project timelines? #FEA #TimeManagement #MeshOptimization #Engineering

The easiest part of building #optimization and #decisionintelligence solutions is writing the code. Yet, I've found few references dealing with the more critical parts of successfully delivering the right solution. Here's my step-by-step approach to increasing the likelihood of delivering a solution with high business impact. 1) Understand the business process: Expanding your view from the problem presented to the process in which it is embedded allows for more holistic solutions and de-risks solving the wrong problem. 2) Interviews with business users and stakeholders: Understanding how users perceive their business process gives a better picture of the communication flow. This is important for change management as you roll out your solution. In addition, it provides a first glimpse to assessing the client's "tech maturity" which influences how you architect your solution. 3) Present an initial solution in plain English: Write a document with a clear problem statement, a high-level description of the solution, and the expected metrics improvements without technical jargon. This serves a double function as an exercise to have mental clarity and a means to #communicate and align with stakeholders. 4) Build a "scrappy" prototype and get it to stakeholders: This is one of the best ways to keep stakeholders engaged, validate that it's on the right path, and streamline change management. The prototype should include the solution, a method to evaluate the relevant metrics, and an interface for stakeholders to interact with your solution. 5) Build a metrics tracking mechanism: Create a dashboard that will be used to review the latest performance metrics of interest so that you can clearly build the story of how your solution is improving them over time as you iterate. 6) Build a CI/CD pipeline: After the prototype's initial validation, build a pipeline that allows you to ship new releases quickly to stakeholders. Establish cadenced checkpoints and demos to get feedback and review metrics. This is an important part of your change management. 7) Pilot: Once the metric improvements have been achieved, run a pilot where you follow how your solution is used as part of the business process. Make any final necessary tweaks to secure adoption. 8) Documenting and closing: Once adoption is satisfactory, close out the project by properly documenting your artifacts for your stakeholders. Include a section identifying other potential improvements to the process and an estimate of their impact for future work. Successful projects go far beyond models and algorithms, they ensure business impact and adoption. This is how we'll make #decisionintelligence the most widely adopted #AI in business. What steps would you also include?

As a Head of People in start-up there are many competing priorities, it’s constant plate spinning. It's really f’in exhausting! 😫 This leads to relentless “context switching” which can turn to mental fatigue and contribute to burnout, which is a very real issue facing the People profession right now On top of that, context switching is also diluting your focus and with it, your impact, which results in a lot of half finished projects, which negatively compound as they stack up I experienced this first hand as a Head of People - believe me, I am far from perfect and I needed to drop the ball a few times before I learned how to catch it… For me, the result of running too many initiatives at once was delays to delivery, poor execution, half baked solutions and unhappy stakeholders… Not, great I am happy to tell you, I found a solution! So, there is no need for you to fall into the same trap of context switching, burnout and stunted impact 🙏🏻 This is going to feel counterintuitive, but trust my experience and my logic when I tell you… When you have 100 things to get done, it’s far better to focus on 1 (or 2) top priorities and execute them well (REALLY well) before moving on to the next thing This may mean being very singular in focus for a longer period of time than feels comfortable, but it is entirely necessary if you want to become a highly impactful Head of People 🤔But Luke, you always talk about working in sprints, MVPs, iterative improvement. Shouldn’t we be spending less time on each project?🤔 Well, yes - sort of But bear in mind there are 2 ways to iteratively improve: 1️⃣ Increase the amount you improve in a single iteration 2️⃣ Or, deliver more iterations to the solution in shorter period of time The better option for use is option 2… With option 2, you 10x the speed of your learning cycle: 🚀10x iterations = 10x learning cycles 🐌1 iteration = 1x learning cycle Focus on 1 problem and iterate 10 times in a shorter period, rather than focusing on 10 problems and iterating each only once in the same time period By delivering 10 iterations to a single project, you have 10x’d your opportunity to learn and improve the solution, which creates a better outcome and increases your impact by an order of magnitude in the same period of time This way of working is going to deliver significantly more value to end users (employees and the business) than if you had spread your time to deliver 10 less significant “improvements” to 10 separate programmes So, with a narrow focus on a single top priority, you can 10x your impact on the most critical projects Which, believe me, is where your value will ultimately be judged Working on 10 projects may make you feel like you're having more impact… but, in reality, you're diluting your impact Better to be able to justify why you haven’t taken action on the other 9 low priority projects by demonstrating 10x impact on the top priority project #peopleexperience #pxasaproduct #agile

🔬 How Are Alloys Developed—And What’s the Problem? Designing new alloys (or materials, in general) usually follows a well-defined process: 1️⃣ Define the design objectives (e.g., maximize strength, improve ductility). 2️⃣ Set constraints based on what’s physically or economically feasible. 3️⃣ Explore possible compositions (experimentally and/or computationally) and optimize based on the initial objectives. 4️⃣ Test, refine, and repeat. But, here’s the catch—this process assumes we know the “right” problem from the start. In reality, as new information comes in, we often realize that our initial assumptions were off. Maybe the real limiting factor isn’t strength but printability. Maybe an overlooked constraint turns out to be crucial. Maybe one key constituent suddenly became scarce due to geopolitical issues. The result? Lots of wasted time reworking the problem mid-way. 💡 What we (Danial Khatamsaz, Joseph Wagner, Brent Vela, Douglas Allaire) did: ✅ We introduce an autonomous design framework that optimizes not just materials, but the very problem formulation itself—iteratively refining objectives in response to data, subject to human preferences. ✅ We applied this to a Mo-Nb-Ti-V-W refractory high-entropy alloy system for turbine blades, balancing trade-offs like ductility, strength, density, and printability. ✅ Instead of forcing a rigid optimization, our system actively searches for the best problem to solve—closing the loop between data and decision-making. Technically, we assume that we can establish a distance metric between problem formulations and construct a kernel function that can, in turn, be used to build Gaussian Processes over the problem space. Once a GP is constructed, one can find the best problem to solve by using traditional Bayesian Optimization. 📈 Why this matters: • Accelerates materials discovery by avoiding costly reformulation cycles. • Shifts optimization from “finding the best material” to “finding the best question to ask”—a paradigm shift in computational materials design. • Brings us closer to the vision of self-driving labs, where AI doesn’t just guide experiments but also redefines their goals dynamically by interacting with subject-matter experts or stakeholders as the problem formulation is refined. The paper is available in the ArXiv: https://lnkd.in/ezWRQuch

Manufacturing Automation – Direction Automation: NOT a straight Line but a Convergence to PRODUCTIVITY! -- Automation is one arrow in the quiver of CONTINUOUS IMPROVEMENT, albeit a powerful step function which by definition shortens cycle times. Automation's INTERDEPENDENCE on all Process Attributes makes it difficult to QUANTIFY the outcome until deployed and include: - Inputs consistency & repeatability - Worker skills & engagement - Rational workflows - Scheduling & co-ordination - All salient characteristics of GOOD manufacturing Temping as a "Master Plan" is, experience shows that it rarely survives contact with the PROCESS as flawed assumptions, tribal knowledge and outdated documentation conspire to expose OBSTACLES that take the plan out of ROI and feasibility. A higher success rate is to be found in ITERATIVE, low cost solutions that follow the PDSA (Plan-Do-Study-Act) loop and incrementally increase PRODUCTIVITY! Automation: NOT a straight Line but a Convergence to PRODUCTIVITY! -- "Sequence: 1.     Focusing on the first bottleneck, a manual, precision sawing operation, we retrofitted the saw with simple semi-automated Product Clamping, Saw Blade Positioning and Blade Actuation, reducing the cycle time by 80% and the operator fatigue was virtually eliminated. In addition, Quality and Accuracy of the cuts went way up as operator variations were eliminated! 2. Simultaneously and concurrently, based on a documented 8 miles per day ... to bring haphazardly arrayed blanks to the saw, the client worked with their supplier to order and stack materials, per order and bar coded, so that a complete and coherent order was presented to the saw and released as a matched set for ... down stream processing, eliminating the walk and previous WIP ... went to zero. 3.     The second bottleneck involved tribal knowledge and specialized skills, the clamping and securing of the matched pieces, and this was addressed with the design and build of custom clamping and positioning system, repeatable every time by ANY operator and the introduction of a stiffener which not only facilitated the automation but also made the final product more reliable and not subject to, in the field adjustments and/or rework. 4.     Several other, worker assist devices were developed ... Outcome: The single metric of reducing the entire process footprint, with the same throughput, by 2/3rds." -- How do you evolve the Automation requirements based on the Process? Your thoughts are appreciated and please SHARE this post if you think your connections will find it of interest. 👉 Comment, follow or connect to COLLABORATE on your automation for increased productivity. Adding value on the WHY, WHAT and HOW of Automation! What are you working on that I can help with? https://lnkd.in/eezHDVXi #industry40 #automation #productivity #robots

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing

Operations leaders in complex environments, here’s the trap I see daily.     We chase a single “best” design when the work demands a family of viable options. Real systems carry constraints and competing goals. You’re not picking a winner; you’re mapping a set of non-dominated choices where improving one goal hurts another. That’s the Pareto front, and ignoring it leads to slow cycles, higher spend, and decisions that don’t hold up under new conditions.     In chemicals, the stakes are clear. The sector is the largest industrial energy consumer, with 925 million metric tons of CO2 reported in 2021, a 5 percent rise year over year. One team addressed this by pairing a process modeling platform with a high-throughput optimization approach and cloud execution. They ran thousands of mixed-integer nonlinear iterations, adjusting parameters simultaneously. The result: lower cyclic byproducts by 45 percent and a 2 percent yield increase, achieved without added capital and with a smaller carbon footprint.     The move to make today: stop tuning one variable at a time. Define your goal set, state the constraints, and let automated, distributed runs search the space for you. Focus on discovering the Pareto front, then pick operating points that fit your current context and risk tolerance.     What to watch for in your own work: if gradients or manual sweeps are your only tools, you’re likely sitting in a local optimum. Shift to simultaneous search and let the data show you the trade-offs.